Spaces:
Running
on
Zero
Running
on
Zero
File size: 28,858 Bytes
43a7079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 |
import numpy as np
import pycuda.autoprimaryctx
import torch
import triton
import triton.language as tl
from flash_attn import flash_attn_varlen_func
from pycuda.compiler import SourceModule
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=4),
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=4),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=3, num_warps=4),
triton.Config({}, num_stages=3, num_warps=8),
triton.Config({}, num_stages=4, num_warps=4),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=5, num_warps=4),
triton.Config({}, num_stages=5, num_warps=8),
],
key=['N_CTX'],
)
@triton.jit
def triton_sparse_fwd_kernel(
Q, K, V, seqlens, sm_scale,
col_count, col_index,
Out,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vn, stride_vk,
stride_oz, stride_oh, stride_om, stride_ok,
Z, H, N_CTX,
NUM_ROWS, MAX_COLS_PRE_ROW,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
dtype: tl.constexpr,
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
seqlen = tl.load(seqlens + off_hz // H)
if start_m * BLOCK_M >= seqlen:
return
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
qo_offset = (off_hz // H) * stride_qz + (off_hz % H) * stride_qh
kv_offset = (off_hz // H) * stride_kz + (off_hz % H) * stride_kh
q_ptrs = Q + qo_offset + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk
k_ptrs = K + kv_offset + offs_d[:, None] * stride_kk
v_ptrs = V + kv_offset + offs_d[None, :] * stride_vk
o_ptrs = Out + qo_offset + offs_m[:, None] * stride_om + offs_d[None, :] * stride_ok
num_cols = tl.load(col_count + off_hz * NUM_ROWS + start_m)
cols_ptr = col_index + (off_hz * NUM_ROWS + start_m) * MAX_COLS_PRE_ROW
# initialize pointer to m and l
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# scale sm_scale by log_2(e) and use
# 2^x instead of exp in the loop because CSE and LICM
# don't work as expected with `exp` in the loop
qk_scale = sm_scale * 1.44269504
# load q: it will stay in SRAM throughout
q = tl.load(q_ptrs)
q = (q * qk_scale).to(dtype)
# loop over k, v and update accumulator
m_mask = offs_m[:, None] < seqlen
split = tl.maximum(num_cols - BLOCK_N, 0) & ~(BLOCK_N - 1)
for start_n in range(0, split, BLOCK_N):
cols = tl.load(cols_ptr + start_n + offs_n)
# -- load k, v --
k = tl.load(k_ptrs + cols[None, :] * stride_kn)
v = tl.load(v_ptrs + cols[:, None] * stride_vn)
# -- compute qk --
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk = tl.where(m_mask, qk, float("-inf"))
qk += tl.dot(q, k)
# -- compute scaling constant --
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(dtype), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
for start_n in range(split, num_cols, BLOCK_N):
n_mask = start_n + offs_n < num_cols
cols = tl.load(cols_ptr + start_n + offs_n, mask=n_mask, other=N_CTX - 1)
causal_mask = cols[None, :] <= offs_m[:, None]
# -- load k, v --
k = tl.load(k_ptrs + cols[None, :] * stride_kn)
v = tl.load(v_ptrs + cols[:, None] * stride_vn)
# -- compute qk --
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk = tl.where(m_mask & causal_mask, qk, float("-inf"))
qk += tl.dot(q, k)
# -- compute scaling constant --
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(dtype), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
# write back O
acc = tl.where(m_mask, acc / l_i[:, None], 0.0)
tl.store(o_ptrs, acc.to(dtype), mask=m_mask)
def triton_sparse_forward(
q, # [BATCH, N_HEADS, N_CTX, D_HEAD]
k, # [BATCH, N_HEADS, N_CTX, D_HEAD]
v, # [BATCH, N_HEADS, N_CTX, D_HEAD]
seqlens, # [BATCH, ]
col_count, # [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
col_index, # [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), MAX_COLS_PRE_ROW]
sm_scale,
block_size_M=64,
block_size_N=64,
) -> torch.Tensor:
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
o = torch.zeros_like(q)
grid = (triton.cdiv(q.shape[2], block_size_M), q.shape[0] * q.shape[1], 1)
num_warps = 4 if (Lk <= 64 or block_size_M <= 64) else 8 # 4
dtype = tl.bfloat16 if q.dtype == torch.bfloat16 else tl.float16
triton_sparse_fwd_kernel[grid](
q, k, v, seqlens, sm_scale,
col_count, col_index,
o,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
o.stride(0), o.stride(1), o.stride(2), o.stride(3),
q.shape[0], q.shape[1], q.shape[2],
col_index.shape[-2], col_index.shape[-1],
BLOCK_M=block_size_M, BLOCK_N=block_size_N,
BLOCK_DMODEL=Lk,
dtype=dtype,
# num_warps=num_warps, num_stages=4,
)
return o
def torch_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M=64):
max_cols_per_row = (seqlens.max().item() + 3) & (-4)
batch_size, num_heads, NNZ_S = slash_indexes.shape
NNZ_V = vertical_indexes.shape[-1]
num_rows = triton.cdiv(max_cols_per_row, block_size_M)
max_cols_per_row = max_cols_per_row
col_count = torch.zeros((batch_size, num_heads, num_rows), dtype=torch.int32)
col_index = torch.zeros((batch_size, num_heads, num_rows, max_cols_per_row), dtype=torch.int32)
for b in range(batch_size):
seqlen = seqlens[b]
for h in range(num_heads):
for m, start_m in enumerate(range(0, seqlen, block_size_M)):
end_m = start_m + block_size_M
tmp_col_count = 0
cursor, s, v = -1, 0, 0
v_idx = vertical_indexes[b, h, v].item()
while s < NNZ_S and slash_indexes[b, h, s] >= end_m:
s += 1
if s < NNZ_S:
s_idx = end_m - slash_indexes[b, h, s].item()
s_range = min(s_idx, block_size_M)
else:
s_idx = seqlen
s_range = 0
while s_idx <= end_m and v_idx < end_m:
if v_idx < s_idx:
if v_idx < s_idx - s_range:
col_index[b, h, m, tmp_col_count] = v_idx
tmp_col_count += 1
v += 1
if v < NNZ_V:
v_idx = vertical_indexes[b, h, v].item()
else:
break
else:
for idx in range(max(cursor, s_idx - s_range), min(s_idx, seqlen)):
col_index[b, h, m, tmp_col_count] = idx
tmp_col_count += 1
cursor = s_idx
s += 1
if s < NNZ_S:
s_idx = end_m - slash_indexes[b, h, s].item()
s_range = min(s_idx, block_size_M)
else:
break
while s_idx <= end_m and s < NNZ_S:
for idx in range(max(cursor, s_idx - s_range), min(s_idx, seqlen)):
col_index[b, h, m, tmp_col_count] = idx
tmp_col_count += 1
cursor = s_idx
s += 1
if s < NNZ_S:
s_idx = end_m - slash_indexes[b, h, s].item()
s_range = min(s_idx, block_size_M)
else:
break
while v_idx < end_m and v < NNZ_V:
if v_idx < s_idx - s_range:
col_index[b, h, m, tmp_col_count] = v_idx
tmp_col_count += 1
v += 1
if v < NNZ_V:
v_idx = vertical_indexes[b, h, v].item()
else:
break
col_count[b, h, m] = tmp_col_count
return col_count.to(seqlens.device), col_index.to(seqlens.device)
PYCUDA_BUILD_INDEX_KERNEL_CODE = '''\
__device__ int min(int x, int y) {
return x < y ? x : y;
}
__device__ int max(int x, int y) {
return x > y ? x : y;
}
__device__ void save_list(int* output, int loop_start, int loop_end, int& offset) {
if (loop_start + 4 >= loop_end) {
for (int idx = loop_start; idx < loop_end; idx++, offset++) {
output[offset] = idx;
}
return;
}
int4 tmp_int4;
int int4_start = ((offset + 3) & (-4)) - offset + loop_start;
int int4_end = ((offset + loop_end - loop_start) & (-4)) - offset + loop_start;
for (int idx = loop_start; idx < int4_start; idx++, offset++) {
output[offset] = idx;
}
for (int idx = int4_start; idx < int4_end; idx += 4, offset += 4) {
tmp_int4.x = idx + 0;
tmp_int4.y = idx + 1;
tmp_int4.z = idx + 2;
tmp_int4.w = idx + 3;
(reinterpret_cast<int4*>(&output[offset]))[0] = tmp_int4;
}
for (int idx = int4_end; idx < loop_end; idx++, offset++) {
output[offset] = idx;
}
}
__global__ void PYCUDA_BUILD_INDEX_KERNEL(
const int* seqlens, // [BATCH, ]
const int* vertical_indexes, // [BATCH, N_HEADS, NNZ_V]
const int* slash_indexes, // [BATCH, N_HEADS, NNZ_S]
int* col_count, // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
int* col_index, // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), N_CTX]
int N_HEADS,
int N_CTX,
int BLOCK_SIZE_M,
int N_ROWS,
int NNZ_V,
int NNZ_S
) {
const int batch_idx = blockIdx.y;
const int head_idx = blockIdx.x;
const int group_idx = blockIdx.z;
int seqlen = seqlens[batch_idx];
int block_idx_m = group_idx * blockDim.x + threadIdx.x;
int start_m = block_idx_m * BLOCK_SIZE_M;
if (start_m >= seqlen) {
return;
}
int end_m = start_m + BLOCK_SIZE_M;
vertical_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_V;
slash_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_S;
int row_offset = (batch_idx * N_HEADS + head_idx) * N_ROWS + block_idx_m;
col_count += row_offset;
col_index += row_offset * N_CTX;
int tmp_col_count = 0, cursor = -1, s = 0, v = 0;
int v_idx = vertical_indexes[v];
/*
int left = 0, right = NNZ_S - 1;
int tmp_s_idx = 0, target = end_m - 1;
s = (left + right) >> 1;
while (left + 1 < right) {
tmp_s_idx = slash_indexes[s];
if (tmp_s_idx > target) {
left = s;
} else if (tmp_s_idx < target) {
right = s;
} else {
break;
}
s = (left + right) >> 1;
}
*/
while (s < NNZ_S && slash_indexes[s] >= end_m) s++;
int s_idx = (s < NNZ_S) ? (end_m - slash_indexes[s]) : seqlen;
int s_range = (s < NNZ_S) ? min(s_idx, BLOCK_SIZE_M) : 0;
while (s_idx <= end_m && v_idx < end_m) {
if (v_idx < s_idx) {
if (v_idx < s_idx - s_range) {
col_index[tmp_col_count] = v_idx;
tmp_col_count++;
}
v++;
if (v < NNZ_V) {
v_idx = vertical_indexes[v];
} else {
break;
}
} else {
save_list(col_index, max(cursor, s_idx - s_range), min(s_idx, seqlen), tmp_col_count);
cursor = s_idx;
s++;
if (s < NNZ_S) {
s_idx = end_m - slash_indexes[s];
s_range = min(s_idx, BLOCK_SIZE_M);
} else {
break;
}
}
}
while (s_idx <= end_m && s < NNZ_S) {
save_list(col_index, max(cursor, s_idx - s_range), min(s_idx, seqlen), tmp_col_count);
cursor = s_idx;
s++;
if (s < NNZ_S) {
s_idx = end_m - slash_indexes[s];
s_range = min(s_idx, BLOCK_SIZE_M);
} else {
break;
}
}
while (v_idx < end_m && v < NNZ_V) {
if (v_idx < s_idx - s_range) {
col_index[tmp_col_count] = v_idx;
tmp_col_count++;
}
v++;
if (v < NNZ_V) {
v_idx = vertical_indexes[v];
} else {
break;
}
}
col_count[0] = tmp_col_count;
}
'''
PYCUDA_BUILD_INDEX_KERNEL = SourceModule(
PYCUDA_BUILD_INDEX_KERNEL_CODE,
options=['-std=c++14', '-O3'],
).get_function(f'PYCUDA_BUILD_INDEX_KERNEL')
def pycuda_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M=64):
max_cols_per_row = (seqlens.max().item() + 3) & (-4)
batch_size, num_heads, NNZ_S = slash_indexes.shape
NNZ_V = vertical_indexes.shape[-1]
num_rows = triton.cdiv(max_cols_per_row, block_size_M)
max_cols_per_row = max_cols_per_row
col_count = torch.zeros((batch_size, num_heads, num_rows), dtype=torch.int32, device=seqlens.device)
col_index = torch.zeros((batch_size, num_heads, num_rows, max_cols_per_row), dtype=torch.int32, device=seqlens.device)
num_threads = 64
PYCUDA_BUILD_INDEX_KERNEL(
seqlens, vertical_indexes, slash_indexes,
col_count, col_index,
np.int32(num_heads), np.int32(max_cols_per_row), np.int32(block_size_M), np.int32(num_rows),
np.int32(NNZ_V), np.int32(NNZ_S),
# grid=(triton.cdiv(num_rows, num_threads), N_HEADS, BATCH),
grid=(num_heads, batch_size, triton.cdiv(num_rows, num_threads)),
block=(num_threads, 1, 1),
)
return col_count, col_index
def make_causal_mask(seqlens, device, context_size):
batch_size = seqlens.shape[0]
arange = torch.arange(context_size, dtype=torch.int32, device=device)
causal_mask = arange[None, None, :, None] >= arange[None, None, None, :]
causal_mask = causal_mask.repeat((batch_size, 1, 1, 1))
for b, seqlen in enumerate(seqlens):
causal_mask[b, :, seqlen:, :] = False
causal_mask[b, :, :, seqlen:] = False
return causal_mask
def make_finegrained_mask(vertical_indexes, slash_indexes, causal_mask, device):
batch_size, num_heads, _ = vertical_indexes.shape
context_size = causal_mask.shape[-1]
arange = torch.arange(context_size, dtype=torch.int32, device=device)
sparse_mask = torch.zeros((batch_size, num_heads, context_size, context_size), dtype=torch.bool, device=device)
for b in range(batch_size):
for h in range(num_heads):
for vertical_index in vertical_indexes[b, h]:
sparse_mask[b, h, :, vertical_index] = True
for slash_index in slash_indexes[b, h]:
sparse_mask[b, h].logical_or_(arange[:, None] - arange[None, :] == slash_index)
sparse_mask.logical_and_(causal_mask)
return sparse_mask
def make_block_mask(col_count, col_index, seqlens, causal_mask, device, block_size_M=64):
batch_size, num_heads, _ = col_count.shape
context_size = causal_mask.shape[-1]
block_mask = torch.zeros((batch_size, num_heads, context_size, context_size), dtype=torch.bool, device=device)
for b in range(batch_size):
for h in range(num_heads):
for m, start_m in enumerate(range(0, seqlens[b], block_size_M)):
end_m = start_m + block_size_M
for c in range(col_count[b, h, m]):
block_mask[b, h, start_m:end_m, col_index[b, h, m, c]] = True
block_mask.logical_and_(causal_mask)
return block_mask
def plot_mask(mask, name, batch=0, head=0):
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(16, 12))
plt.clf()
mask = mask[batch, head].cpu().numpy()
sns.heatmap(mask)
plt.savefig(name)
@triton.jit
def triton_dense_fwd_kernel(
Q, K, V, seqlens, sm_scale,
Out,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vn, stride_vk,
stride_oz, stride_oh, stride_om, stride_ok,
Z, H, N_CTX,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
dtype: tl.constexpr,
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
seqlen = tl.load(seqlens + off_hz // H)
if start_m * BLOCK_M >= seqlen:
return
qo_offset = (off_hz // H) * stride_qz + (off_hz % H) * stride_qh
kv_offset = (off_hz // H) * stride_kz + (off_hz % H) * stride_kh
Q_block_ptr = tl.make_block_ptr(
base=Q + qo_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_qm, stride_qk),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
K_block_ptr = tl.make_block_ptr(
base=K + kv_offset,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_kk, stride_kn),
offsets=(0, 0),
block_shape=(BLOCK_DMODEL, BLOCK_N),
order=(0, 1)
)
V_block_ptr = tl.make_block_ptr(
base=V + kv_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_vn, stride_vk),
offsets=(0, 0),
block_shape=(BLOCK_N, BLOCK_DMODEL),
order=(1, 0)
)
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
# initialize pointer to m and l
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# scale sm_scale by log_2(e) and use
# 2^x instead of exp in the loop because CSE and LICM
# don't work as expected with `exp` in the loop
qk_scale = sm_scale * 1.44269504
# load q: it will stay in SRAM throughout
q = tl.load(Q_block_ptr)
q = (q * qk_scale).to(dtype)
# loop over k, v and update accumulator
lo = 0
hi = (start_m + 1) * BLOCK_M
m_mask = offs_m[:, None] < seqlen
for start_n in range(lo, hi, BLOCK_N):
n_mask = (start_n + offs_n[None, :]) <= offs_m[:, None]
# -- load k, v --
k = tl.load(K_block_ptr)
v = tl.load(V_block_ptr)
# -- compute qk --
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk = tl.where(m_mask & n_mask, qk, float("-inf"))
qk += tl.dot(q, k)
# -- compute scaling constant --
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(dtype), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
# update pointers
K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
# write back O
acc = tl.where(m_mask, acc / l_i[:, None], 0.0)
O_block_ptr = tl.make_block_ptr(
base=Out + qo_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_om, stride_ok),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
tl.store(O_block_ptr, acc.to(dtype), mask=m_mask)
def triton_dense_forward(q, k, v, seqlens, sm_scale, block_size_M=128, block_size_N=64) -> torch.Tensor:
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
o = torch.zeros_like(q)
grid = (triton.cdiv(q.shape[2], block_size_M), q.shape[0] * q.shape[1], 1)
num_warps = 4 if Lk <= 64 else 8 # 4
dtype = tl.bfloat16 if q.dtype == torch.bfloat16 else tl.float16
triton_dense_fwd_kernel[grid](
q, k, v, seqlens, sm_scale,
o,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
o.stride(0), o.stride(1), o.stride(2), o.stride(3),
q.shape[0], q.shape[1], q.shape[2],
BLOCK_M=block_size_M, BLOCK_N=block_size_N,
BLOCK_DMODEL=Lk,
dtype=dtype,
num_warps=num_warps, num_stages=4,
)
return o
def flash_attn_forward(q, k, v, seqlens, sm_scale, context_size) -> torch.Tensor:
return flash_attn_varlen_func(
q,
k,
v,
cu_seqlens_q=seqlens,
cu_seqlens_k=seqlens,
max_seqlen_q=context_size,
max_seqlen_k=context_size,
dropout_p=0.0,
softmax_scale=sm_scale,
causal=True,
)
def torch_forward(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: torch.Tensor,
sm_scale: float,
) -> torch.Tensor:
p = torch.einsum(f'bhmk, bhnk -> bhmn', query, key) * sm_scale
p = p.where(mask, -torch.inf)
p_max = p.max(-1, keepdim=True).values
p_max = torch.where(p_max < 0, 0.0, p_max)
p_exp = torch.exp(p - p_max)
s = p_exp / (p_exp.sum(-1, keepdim=True) + 1e-6)
out = torch.einsum(f'bhmn, bhnk -> bhmk', s, value)
return out
def profile(fn, total_flops, tag, warmup=25, rep=100):
ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
gflops = total_flops / ms * 1e-9
print(f'{tag}: {ms:.3f} ms | {gflops:.3f} GFLOP/s')
def test_flash_attention(
seqlens=None,
vertical_indexes=None,
slash_indexes=None,
dtype=torch.float16,
device="cuda",
torch_test=True,
batch_size=4,
num_heads=32,
context_size=1024,
head_dim=128,
sparsity=0.995,
block_size_M=64,
block_size_N=64,
):
print('========================================')
print(f'BATCH={batch_size}, N_CTX={context_size}, N_HEADS={num_heads}, D_HEAD={head_dim}')
q = torch.randn((batch_size, num_heads, context_size, head_dim), dtype=dtype, device=device)
k = torch.randn((batch_size, num_heads, context_size, head_dim), dtype=dtype, device=device)
v = torch.randn((batch_size, num_heads, context_size, head_dim), dtype=dtype, device=device)
if seqlens is None:
seqlens = torch.randint(context_size // 2, context_size, (batch_size, ), dtype=torch.int32, device=device)
else:
seqlens = torch.tensor(seqlens, dtype=torch.int32, device=device)
dense_mask_nnz = seqlens.to(torch.float32).square().sum().item() * num_heads / 2
sm_scale = head_dim ** -0.5
causal_mask = make_causal_mask(seqlens, device, context_size)
if torch_test:
ref_o_dense = torch_forward(q, k, v, causal_mask, sm_scale)
if vertical_indexes is None or slash_indexes is None:
nnz = int((1 - sparsity) * context_size)
vertical_indexes = torch.stack([
torch.stack([
torch.randperm(seqlen, dtype=torch.int32, device=device)[:nnz].sort(descending=False)[0]
for _ in range(num_heads)
])
for seqlen in seqlens
])
slash_indexes = torch.concatenate([
torch.stack([
torch.stack([
torch.randperm(seqlen - 1, dtype=torch.int32, device=device)[:nnz].sort(descending=True)[0] + 1
for _ in range(num_heads)
])
for seqlen in seqlens
]),
torch.zeros((batch_size, num_heads, 1), dtype=torch.int32, device=device)
], dim=-1)
col_count, col_index = pycuda_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M)
if torch_test:
col_count_ref, col_index_ref = torch_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M)
# import ipdb; ipdb.set_trace()
torch.testing.assert_close(col_count_ref, col_count)
torch.testing.assert_close(col_index_ref, col_index)
sparse_mask_nnz = col_count.to(torch.float32).sum().item() * block_size_M
print(f'block mask sparsity: {1 - sparse_mask_nnz / dense_mask_nnz}')
pycuda_build_index_fn = lambda: pycuda_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M)
profile(pycuda_build_index_fn, 0., 'pycuda-index')
if torch_test:
finegrained_mask = make_finegrained_mask(vertical_indexes, slash_indexes, causal_mask, device)
block_mask = make_block_mask(col_count, col_index, seqlens, causal_mask, device, block_size_M)
# plot_mask(finegrained_mask, 'mask.png', 2, 26)
# plot_mask(block_mask, 'mask-1.png', 2, 26)
ref_o_sparse = torch_forward(q, k, v, block_mask, sm_scale)
triton_dense_fn = lambda: triton_dense_forward(q, k, v, seqlens, sm_scale)
output = triton_dense_fn()
if torch_test:
torch.testing.assert_close(output, ref_o_dense, atol=1e-2, rtol=0)
profile(triton_dense_fn, 2. * head_dim * dense_mask_nnz, 'triton-dense')
triton_sparse_fn = lambda: triton_sparse_forward(q, k, v, seqlens, col_count, col_index, sm_scale, block_size_M, block_size_N)
output = triton_sparse_fn()
if torch_test:
torch.testing.assert_close(output, ref_o_sparse, atol=1e-2, rtol=0)
profile(triton_sparse_fn, 2. * head_dim * sparse_mask_nnz, 'triton-sparse')
q = q.swapaxes(1, 2).contiguous()
k = k.swapaxes(1, 2).contiguous()
v = v.swapaxes(1, 2).contiguous()
q = torch.concatenate([q[i, :seqlen, :, :] for i, seqlen in enumerate(seqlens)])
k = torch.concatenate([k[i, :seqlen, :, :] for i, seqlen in enumerate(seqlens)])
v = torch.concatenate([v[i, :seqlen, :, :] for i, seqlen in enumerate(seqlens)])
seqlens = torch.nn.functional.pad(torch.cumsum(seqlens, dim=0, dtype=torch.int32), (1, 0))
flash_fn = lambda: flash_attn_forward(q, k, v, seqlens, sm_scale, context_size)
output = flash_fn()
output = torch.stack([
torch.nn.functional.pad(
output[seqlens[i]:seqlens[i + 1], :, :],
(0, 0, 0, 0, 0, context_size + seqlens[i] - seqlens[i + 1])
)
for i in range(batch_size)
]).swapaxes(1, 2).contiguous()
if torch_test:
torch.testing.assert_close(output, ref_o_dense, atol=1e-2, rtol=0)
profile(flash_fn, 2. * head_dim * dense_mask_nnz, 'flash-dense')
print('========================================\n')
def pit_sparse_flash_attention_forward(
query: torch.Tensor, # [BATCH, N_HEADS, N_CTX, D_HEAD]
key: torch.Tensor, # [BATCH, N_HEADS, N_CTX, D_HEAD]
value: torch.Tensor, # [BATCH, N_HEADS, N_CTX, D_HEAD]
v_idx: torch.Tensor, # [BATCH, N_HEADS, NNZ_V]
s_idx: torch.Tensor, # [BATCH, N_HEADS, NNZ_S]
block_size_M: int = 64,
block_size_N: int = 64,
):
q_len = query.shape[2]
pad = block_size_M - (query.shape[2] & (block_size_M - 1))
query = torch.nn.functional.pad(query, [0, 0, 0, pad, 0, 0, 0, 0])
key = torch.nn.functional.pad(key, [0, 0, 0, pad, 0, 0, 0, 0])
value = torch.nn.functional.pad(value, [0, 0, 0, pad, 0, 0, 0, 0])
batch_size, num_heads, context_size, head_dim = query.shape
v_idx = v_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=False)[0]
s_idx = s_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=True)[0]
seqlens = torch.tensor([context_size], dtype=torch.int32, device=query.device)
sm_scale = head_dim ** -0.5
col_count, col_index = pycuda_build_index(seqlens, v_idx, s_idx, block_size_M)
out = triton_sparse_forward(query, key, value, seqlens, col_count, col_index, sm_scale, block_size_M, block_size_N)[...,:q_len,:]
return out
|